Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
First Clinical College, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Endocrinol (Lausanne). 2024 May 10;15:1385324. doi: 10.3389/fendo.2024.1385324. eCollection 2024.
The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data.
2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software.
Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed.
Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.
甲状腺癌的发病率增长迅速，手术是其最主要的治疗手段。对于单侧 cN0 甲状腺乳头状癌患者，是否对侧中央区淋巴结进行清扫仍存在争议。本研究旨在利用患者的人口统计学和临床数据，建立基于机器学习的对侧中央区淋巴结转移的预测模型。
回顾性分析了 2225 例来自武汉协和医院的单侧 cN0 甲状腺乳头状癌患者的临床病理特征。比较了有对侧中央区淋巴结转移和无对侧中央区淋巴结转移的患者之间的临床和病理特征。基于这些患者构建了 6 种机器学习模型，并通过准确性、敏感度、特异度、受试者工作特征曲线下面积和决策曲线分析进行比较。然后使用中国分化型甲状腺癌研究的数据对选定的模型进行验证。所有统计分析和模型构建均由 R 软件完成。
男性、最大直径大于 1cm、多灶性、同侧中央区淋巴结转移和年龄小于 50 岁是对侧中央区淋巴结转移的独立危险因素。随机森林模型的表现优于其他模型，并在外部验证队列中得到验证。构建了一个网络计算器。
性别、最大直径、多灶性、同侧中央区淋巴结转移和年龄应考虑用于对侧中央区淋巴结清扫。基于随机森林模型的网络计算器可能有助于临床决策。
